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Background:Candida spp can cause a variety of infections known as candidiasis, ranging from severe invasive infections to superficial mucosal infections of the mouth and vagina. Fluconazole, a triazole antifungal, is commonly prescribed to treat candidiasis but increasing fluconazole resistance is a growing concern for several Candida spp. Although C. albicans has historically been the most common cause of candidiasis, other species are increasingly common and antifungal resistance is more prevalent in these non-albicans species, including C. glabrata, C. parapsilosis, and C. tropicalis, which were the focus of this analysis. Methods: We used the PINC AI healthcare data (PHD) database to examine fluconazole resistance for inpatient isolates between 2012 and 2021 from 187 US acute-care hospitals with at least 1 Candida spp culture with a fluconazole susceptibility result over the entire period. We calculated annual percentage fluconazole resistance for C. glabrata, C. tropicalis, and C. parapsilosis isolates using the clinical laboratory interpretation for resistance. Results: We identified 4,264 C. glabrata, 2,482 C. parapsilosis, and 2,283 C. tropicalis isolates between 2012 and 2021 with susceptibility results. The percentage of C. glabrata isolates resistant to fluconazole doubled between 2020 and 2021 (14.6% vs 29.3%) (Fig. 1a). The percentage of C. parapsilosis isolates resistant to fluconazole steadily increased since 2017 (Fig. 1b), with an 82% increase in 2021 compared with 2020 (3.8% in 2020 vs 6.9% in 2021). Fluconazole resistance among C. tropicalis isolates varied over the years, with a 0.3% decrease in 2021 from 2020 (Fig. 1c). Of hospitals reporting at least 1 result each year 2020–2021, 44% observed an increase in the proportion of C. glabrata isolates resistant to fluconazole in 2021 compared to 2020. Conclusions: Our analysis highlights a concerning increase in fluconazole resistance among C. glabrata and C. parapsilosis isolates in 2021 compared with previous years. Further investigation of the observed increases in fluconazole resistance among these Candida spp could provide further insight on potential drivers of resistance or limitations in reported results from large databases. More analyses are needed to understand rates, sites of Candida infections, and risk factors (eg, antifungal exposure) associated with resistance.
Background: Community-acquired pneumonia (CAP) is a common indication for antibiotic prescribing in hospitalized patients. Professional societies’ clinical guidelines recommend specific antibiotics for empiric treatment of CAP based on clinical factors. Manual assessments of appropriateness are time-consuming and are often conducted on a smaller scale. We evaluated empiric antibiotic selection among a large cohort of adults hospitalized with CAP using electronic health records. Methods: In this study, we used the PINC-AI healthcare database to define a cohort of adults hospitalized with CAP from 2013 to 2020. CAP was identified by International Classification of Diseases (ICD) diagnosis codes. Exclusions were applied to identify uncomplicated CAP (Fig. 1). Treatment was only evaluated if a chest radiograph or computerized tomography (CT) scan was charged during the first 2 days of hospitalization, otherwise it was considered an inadequate CAP evaluation. Administrative billing data were used to identify antibiotics charged within the first 2 days of hospitalization. Empiric guideline-recommended treatment was determined based on 2019 CAP guidelines and more recent studies. Patients who received nonrecommended treatment were evaluated for antibiotic allergies in the current hospitalization or methicillin-resistant Staphylococcus aureus (MRSA) colonization or infection in the year prior or on admission using International Classification of Disease, Tenth Revision (ICD-10) diagnosis codes. Results: We identified 4.47 million adult hospitalizations with CAP from 2013 to 2020; 32% (1.43 million) were included in this analysis (Fig. 1). Among discharges with adequate CAP evaluation (1.37 million), 59.7% received recommended antibiotics in the first 2 days of hospitalization, ranging from 62.6% in 2013 to 57.5% in 2019. Overall, 34.8% of our study population received a nonrecommended antibiotic without documentation of an antibiotic allergy or MRSA colonization (2013: 32.5%; 2018: 36.7%) (Fig. 2). Most patients in our study population received >1 antibiotic (92.3%) in the first 2 days of hospitalization. The most common antibiotics among patients receiving recommended treatment were ceftriaxone (74.2% of patients receiving recommended treatment), azithromycin (67.2%), and levofloxacin (31.8%) (Fig. 3a). The most common nonrecommended antibiotics were vancomycin (57.2% of patients receiving nonrecommended treatment), piperacillin-tazobactam (48.1%), and cefepime (25.7%) (Fig. 3b). From 2013 to 2020, cefepime charges consistently increased among CAP patients treated with nonrecommended antibiotics, whereas levofloxacin charges consistently decreased among CAP patients treated with only recommended antibiotics. Conclusions: Approximately one-third of patients with uncomplicated CAP received nonrecommended empiric antibiotics, and from 2013 to 2020 that proportion increased by 9%. Additional strategies are needed to help identify opportunities to optimize antibiotic selection among patients with CAP.
Background: The 2014 US National Strategy for Combating Antibiotic-Resistant Bacteria aimed to reduce inappropriate inpatient antibiotic use by 20% for monitored conditions, such as community-acquired pneumonia (CAP), by 2020. Clinical guidelines recommend treating uncomplicated CAP with a minimum of 5 days of antibiotic therapy. Total length of therapy (LOT) >7 days or >3 days after clinical improvement is rarely necessary. In a previous study estimating LOT in uncomplicated CAP patients, 71% of patients ≥65 years exceeded recommended duration of antibiotics in 2012–2013 (Yi et al, 2018). We evaluated annual trends in LOT in adults ≥65 years hospitalized with uncomplicated CAP from 2013 to 2020. Methods: We conducted a retrospective cohort study among patients in the CMS database with a primary diagnosis of bacterial or unspecified pneumonia using International Classification of Diseases 9th and 10th Revision codes, length of stay (LOS) of 2–10 days, discharged home with self-care, and not rehospitalized in the 3 days following discharge. Discharge home was used as a surrogate for clinical improvement. Because inpatient LOT is not available in CMS data, we used linear regression to model inpatient LOT as a function of LOS using data on CAP patients ≥65 years from the PINC AI healthcare database. Postdischarge LOT was based on prescriptions filled following discharge. Total LOT was calculated by summing estimated inpatient LOT and actual postdischarge LOT (Fig. 1). Total LOT >7 days and postdischarge LOT >3 days were considered indicators of likely excessive LOT. We reported trends in the proportion of patients with likely excessive LOT during the study period. Results: From 2013 through 2020, there were 400,928 uncomplicated CAP hospitalizations among patients aged ≥65 years. Patients were more likely to be female (55%), and they had a median age of 76 years and a median LOS of 3 days. The median total LOT decreased from 9.5 days in 2013 to 7.7 days in 2020. The proportion of patients with total LOT >7 days decreased from 68% in 2013 to 50% in 2020 (% change, −27%); the proportion with postdischarge LOT >3 days decreased from 73% in 2013 to 62% in 2020 (% change, −16%) (Fig. 2). Conclusions: Likely excessive total LOT for adults ≥65 years hospitalized with uncomplicated CAP decreased by 27% in 2020, a considerable improvement from 2013. However, the high proportion of patients with likely excessive postdischarge LOT in 2020 (62%) demonstrates the need for antibiotic stewardship to optimize prescribing at hospital discharge.
Among nursing home outbreaks of coronavirus disease 2019 (COVID-19) with ≥3 breakthrough infections when the predominant severe acute respiratory coronavirus virus 2 (SARS-CoV-2) variant circulating was the SARS-CoV-2 δ (delta) variant, fully vaccinated residents were 28% less likely to be infected than were unvaccinated residents. Once infected, they had approximately half the risk for all-cause hospitalization and all-cause death compared with unvaccinated infected residents.
The coronavirus disease 2019 pandemic caused substantial changes to healthcare delivery and antibiotic prescribing beginning in March 2020. To assess pandemic impact on Clostridioides difficile infection (CDI) rates, we described patients and trends in facility-level incidence, testing rates, and percent positivity during 2019–2020 in a large cohort of US hospitals.
We estimated and compared rates of community-onset CDI (CO-CDI) per 10,000 discharges, hospital-onset CDI (HO-CDI) per 10,000 patient days, and C. difficile testing rates per 10,000 discharges in 2019 and 2020. We calculated percent positivity as the number of inpatients diagnosed with CDI over the total number of discharges with a test for C. difficile. We used an interrupted time series (ITS) design with negative binomial and logistic regression models to describe level and trend changes in rates and percent positivity before and after March 2020.
In pairwise comparisons, overall CO-CDI rates decreased from 20.0 to 15.8 between 2019 and 2020 (P < .0001). HO-CDI rates did not change. Using ITS, we detected decreasing monthly trends in CO-CDI (−1% per month, P = .0036) and HO-CDI incidence (−1% per month, P < .0001) during the baseline period, prior to the COVID-19 pandemic declaration. We detected no change in monthly trends for CO-CDI or HO-CDI incidence or percent positivity after March 2020 compared with the baseline period.
While there was a slight downward trajectory in CDI trends prior to March 2020, no significant change in CDI trends occurred during the COVID-19 pandemic despite changes in infection control practices, antibiotic use, and healthcare delivery.
Background: Previously, we reported decreasing postadmission urine-culture rates in hospitalized patients between 2012 and 2017, indicating a possible decrease in hospital-onset urinary tract infections or changes in diagnostic practices in acute-care hospitals (ACHs). In this study, we re-evaluated the trends using more recent data from 2017–2020 to assess whether new trends in hospital urine-culturing practices had emerged. Method: We conducted a longitudinal analysis of monthly urine-culture rates using microbiology data from 355 ACHs participating in the Premier Healthcare Database in 2017–2020. All cultures from the urinary tract collected on or before day 3 were defined as admission urine cultures and those collected on day 4 or later were defined as postadmission urine cultures. We included discharges from months where a hospital reported at least 1 urine culture with microbiology and antimicrobial susceptibility test results. Annual estimates of rates of admission culture and postadmission urine-culture rates were assessed using general estimating equation models with a negative binomial distribution accounting for hospital-level clustering and adjusting for hospital bed size, teaching status, urban–rural designation, discharge month, and census division. Estimated rate for each year (2018, 2019, and 2020) was compared to previous year’s estimated rate using rate ratios (RRs) and 95% confidence intervals (CIs) generated through the multivariable GEE models. Results: From 2017 to 2020, we included 8.7 million discharges and 1,943,540 urine cultures, of which 299,013 (15.4%) were postadmission urine cultures. In 2017–2020, unadjusted admission culture rates were 20.0, 19.6, 17.9, and 18.2 per 100 discharges respectively; similarly, unadjusted postadmission urine-culture rates were 8.6, 7.8, 7.0, and 7.5 per 1,000 patient days. In the multivariable analysis, adjusting for hospital characteristics, no significant changes in admission urine-culture rates were detected during 2017–2019; however, in 2020, admission urine-culture rates increased 6% compared to 2019 (RR, 1.06; 95% CI, 1.02–1.09) (Fig. 1). Postadmission urine-culture rates decreased 4% in 2018 compared to 2017 (RR, 0.96; 95% CI, 0.91–0.99) and 8% in 2019 compared to 2018 (RR, 0.92; 95% CI, 0.87–0.96). In 2020, postadmission urine-culture rates increased 10% compared to 2019 (RR, 1.10; 95% CI, 1.06–1.14) (Fig. 2). Factors significantly associated with postadmission urine-culture rates included discharge month and hospital bed size. For admission urine cultures, discharge month was the only significant factor. Conclusions: Between 2017–2019, postadmission urine-culture rates continued a decreasing trend, while admission culture rates remained unchanged. However, in 2020 both admission and postadmission urine culture rates increased significantly in comparison to 2019.
We reviewed trimethoprim-sulfamethoxazole antibiotic susceptibility testing data among Staphylococcus aureus using 3 national inpatient databases. In all 3 databases, we observed an increases in the percentage of methicillin-resistant Staphylococcus aureus that were not susceptible to trimethoprim-sulfamethoxazole. Providers should select antibiotic regimens based on local resistance patterns and should report changes to the public health department.
Multisystem inflammatory syndrome in adults (MIS-A) is a hyperinflammatory illness related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The characteristics of patients with this syndrome and the frequency with which it occurs among patients hospitalised after SARS-CoV-2 infection are unclear. Using the Centers for Disease Control and Prevention case definition for MIS-A, we created ICD-10-CM code and laboratory criteria to identify potential MIS-A patients in the Premier Healthcare Database Special COVID-19 Release, a database containing patient-level information on hospital discharges across the United States. Modified MIS-A criteria were applied to hospitalisations with discharge from March to December 2020. The proportion of hospitalisations meeting electronic health record criteria for MIS-A and descriptive statistics for patients in the potential MIS-A cohort were calculated. Of 34 515 SARS-CoV-2-related hospitalisations with complete clinical and laboratory data, 53 met modified criteria for MIS-A (0.15%). The median age was 62 years (IQR 52–74). Most patients met the severe cardiac illness criterion through either myocarditis (66.0%) or new-onset heart failure (35.8%). A total of 79.2% of patients required ICU admission, while 43.4% of patients in the cohort died. MIS-A appears to be a rare but severe outcome of SARS-CoV-2 infection. Additional studies are needed to investigate how this syndrome differs from severe coronavirus disease 2019 (COVID-19) in adults.
Previously reported associations between hospital-level antibiotic use and hospital-onset Clostridioides difficile infection (HO-CDI) were reexamined using 2012–2018 data from a new cohort of US acute-care hospitals. This analysis revealed significant positive associations between total, third-generation, and fourth-generation cephalosporin, fluoroquinolone, carbapenem, and piperacillin-tazobactam use and HO-CDI rates, confirming previous findings.
Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) is an important cause of healthcare-associated infections with limited treatment options and high mortality. To describe risk factors for mortality, we evaluated characteristics associated with 30-day mortality in patients with CRAB identified through the Emerging Infections Program (EIP). Methods: From January 2012 through December 2017, 8 EIP sites (CO, GA, MD, MN, NM, NY, OR, TN) participated in active, laboratory-, and population-based surveillance for CRAB. An incident case was defined as patient’s first isolation in a 30-day period of A. baumannii complex from sterile sites or urine with resistance to ≥1 carbapenem (excluding ertapenem). Medical records were abstracted. Patients were matched to state vital records to assess mortality within 30 days of incident culture collection. We developed 2 multivariable logistic regression models (1 for sterile site cases and 1 for urine cases) to evaluate characteristics associated with 30-day mortality. Results: We identified 744 patients contributing 863 cases, of which 185 of 863 cases (21.4%) died within 30 days of culture, including 113 of 257 cases (44.0%) isolated from a sterile site and 72 of 606 cases (11.9%) isolated from urine. Among 628 hospitalized cases, death occurred in 159 cases (25.3%). Among hospitalized fatal cases, death occurred after hospital discharge in 27 of 57 urine cases (47.4%) and 21 of 102 cases from sterile sites (20.6%). Among sterile site cases, female sex, intensive care unit (ICU) stay after culture, location in a healthcare facility, including a long-term care facility (LTCF), 3 days before culture, and diagnosis of septic shock were associated with increased odds of death in the model (Fig. 1). In urine cases, age 40–54 or ≥75 years, ICU stay after culture, presence of an indwelling device other than a urinary catheter or central line (eg, endotracheal tube), location in a LTCF 3 days before culture, diagnosis of septic shock, and Charlson comorbidity score ≥3 were associated with increased odds of mortality (Fig. 2). Conclusion: Overall 30-day mortality was high among patients with CRAB, including patients with CRAB isolated from urine. A substantial fraction of mortality occurred after discharge, especially among patients with urine cases. Although there were some differences in characteristics associated with mortality in patients with CRAB isolated from sterile sites versus urine, LTCF exposure and severe illness were associated with mortality in both patient groups. CRAB was associated with major mortality in these patients with evidence of healthcare experience and complex illness. More work is needed to determine whether prevention of CRAB infections would improve outcomes.
Background: Studies on the effectiveness of hospital-based interventions often measure hospital-onset infections as the outcome of interest. However, hospital-associated infections may manifest after patient discharge (classified as hospital-associated community-onset, HACO), and the epidemiology may vary by antibiotic resistance (AR) profile. We examined the epidemiology and trends of HACO infections of AR and non–antibiotic-resistant (non-AR) bacteria. Methods: We included clinical community-onset (CO) cultures (obtained sooner than or on day 3 of hospitalization) yielding the bacterial species of interest among hospitalized patients in 260 hospitals in the Premier Healthcare Database from 2012 to 2017. HACO infections were defined as CO cultures in a patient who had a previous hospitalization in the same hospital within 30 days. We examined methicillin resistance among Staphylococcus aureus (MRSA), vancomycin resistance among Enterococcus spp (VRE), carbapenem resistance among Enterobacteriaceae (E. coli, Klebsiella spp, and Enterobacter spp) (CRE), extended-spectrum cephalosporin resistance suggestive of extended-spectrum β-lactamase (ESBL) production in Enterobacteriaceae, carbapenem resistance among Acinetobacter spp (CRAsp), and carbapenem resistance among Pseudomonas aeruginosa (CRPA). We described the proportion of CO infections that were HACO, the proportion of HACO infections from sterile sites, overall HACO rates, and annual trends for sensitive and resistant phenotypes. Generalized estimating equation regression models that accounted for hospital-level clustering were used to estimate annual trends controlling for hospital characteristics and month of discharge. Results: The rate of HACO infections by pathogen ranged from 0.78 to 38.76 per 10,000 hospitalizations; 7%–34% were sterile site infections (Table 1). For each bacterial pathogen, a significantly higher proportion of AR CO infections had a previous hospitalization compared to non-AR CO infections (all χ2, P < .05). The annual trends for AR and non-AR HACO infections between 2012 and 2017 were significantly decreasing for most pathogens, except ESBL HACO infections. Conclusions: Even when using a definition limited to readmission to the same hospital, HACO infections occur commonly with differing rates by pathogen and antibiotic resistance profile. Although these rates are decreasing for most of the pathogens studied, improving surveillance and identifying prevention strategies for these infections are necessary to further reduce the burden of hospital-associated infections.
Background: In recent years, the historic declines in the incidence of methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) in the United States have slowed. We examined trends in the incidence of community-onset (CO) MRSA BSIs among hospitalized persons with and without substance-use diagnoses. Methods: Using data from >200 US hospitals reporting to the Premier Healthcare Database (PHD) during 2012–2017, we conducted a retrospective study among hospitalized persons aged ≥18 years. MRSA BSIs with substance use were defined as hospitalizations having both a blood culture positive for MRSA and at least 1 International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) or ICD-10-CM diagnostic code for substance use including opioids, cocaine, amphetamines, or other substances (excluding cannabis, alcohol, and nicotine). MRSA BSIs were considered community onset when a positive blood culture was collected within 3 days of admission. We assessed annual trends and described characteristics of CO MRSA BSI hospitalizations, stratified by substance use. Results: Of 20,049 MRSA BSIs from 2012 to 2017, 17,634 (88%) were CO. Overall, MRSA BSI incidence decreased 7%, from 178.5 to 166.2 per 100,000 hospitalizations during the study period; However, CO MRSA BSI rates remained stable (152.7 to 149.9 per 100,000 hospitalizations). Among CO MRSA BSIs, 1,838 (10%) were BSIs with substance-use diagnoses; the incidence of CO MRSA BSIs with substance use increased 236% (from 8.2 to 27.6 per 100,000 hospitalizations) and represented a greater proportion of the CO MRSA rate over the study period (Fig. 1). The incidence of CO MRSA BSIs without substance use decreased 15% (from 144.5 to 122.4 per 100,000 hospitalizations). Patients with CO MRSA BSIs with substance use were younger (median, 40 vs 65 years), more likely to be female (50% vs 40%), white (79% vs 69%), and to leave against medical advice (15% vs 1%). Among patients not leaving against medical advice, CO BSI patients with substance-use diagnoses had longer lengths of stay (median, 11 vs 9 days), lower in-hospital mortality (9% vs 14%), and higher hospitalization costs (median, $22,912 vs $17,468) compared to patients without substance-use diagnoses. Conclusions: Although the overall CO MRSA BSI rate remained unchanged from 2012 to 2017, infections with substance use diagnoses increased >3-fold, and infections without substance use diagnoses decreased. These data suggest that the emergence of MRSA associated with substance-use diagnoses threatens potential progress in reducing the incidence of CO MRSA infections. Additional strategies may be needed to prevent MRSA BSI in patients with substance-use diagnoses, and to maintain national progress in the reduction of MRSA infections overall.
Background: Hepatitis C virus (HCV) infection is endemic in Mongolia, with reported prevalence of HCV antibody (anti-HCV) positivity of 11%–16% in the adult population. Healthcare-related risk factors associated with development of acute HCV infection have not been evaluated in this population. Methods:We conducted a prospective, matched case-control study to identify risk factors associated with acute HCV infection in Ulaanbaatar, Mongolia. Cases were aged 18 years with discrete onset of symptoms consistent with acute viral hepatitis as well as jaundice or elevated serum alanine aminotransferase (ALT) levels who were admitted to the National Center for Communicable Diseases during January–October, 2019. Cases were both anti-HCV and HCV RNA positive and tested negative for acute hepatitis A, B, and E. Controls were randomly selected from the Population and Household Database, a national registry of all citizens, and were matched by age and gender. Data collection covered healthcare-associated and other risk factors in the 6 months before symptom onset (cases) or interview date (controls). Adjusted measures of association comparing cases and their matched controls were obtained using a multivariate conditional logistic regression model. Results: We enrolled 35 case patients and 104 controls. Median age of all participants was 44 (range, 23–63) years and 19% (27 of 139) were men. All case patients reported jaundice and loss of appetite; most cases reported nausea, malaise, and abdominal pain (97%, 91%, and 83%, respectively). The median ALT level among case patients was 1,185 IU/L (range, 212–3,349). Case patients were more likely than controls to have been admitted as inpatients (matched odds ratio [mOR], 4.3; 95% CI, 1.5–11.9), to have visited an outpatient clinic (mOR, 3.6; 95% CI, 1.3–10.2), to have had phlebotomy (mOR, 3.3; 95% CI, 1.5–7.5) or endoscopy (mOR, 10.7; 95% CI, 2.2–51.2) as an outpatient procedure, and to have received an injection outside of healthcare settings (mOR, 2.2; 95% CI, 1.0–5.1). Cases were also more likely to have lived in a yurt (mOR, 2.3; 95% CI, 1.0–5.0) and to have lived with persons diagnosed with HCV infection (mOR, 3.0; 95% CI, 1.1–7.9). In a multivariate model, only outpatient endoscopy (adjusted OR, 10.8; 95% CI, 1.7–69.6) was significantly associated with case status. Conclusions: This is the first study to evaluate risk factors for acute HCV infection among adults in Ulaanbaatar, Mongolia. Outpatient endoscopy was associated with new HCV infections in this population; evaluation of gaps in infection control practices at settings providing these services are needed to prevent transmission of communicable diseases, including hepatitis C.
Background: Epidemiological studies have utilized administrative discharge diagnosis codes to identify methicillin-resistant and methicillin-sensitive Staphylococcus aureus (MRSA and MSSA) infections and trends, despite debate regarding the accuracy of utilizing codes for this purpose. We assessed the sensitivity and positive predictive value (PPV) of MRSA- and MSSA-specific diagnosis codes, trends, characteristics, and outcomes of S. aureus hospitalizations by method of identification. Methods: Clinical micro biology results and discharge data from geographically diverse US hospitals participating in the Premier Healthcare Database from 2012–2017 were used to identify monthly rates of MRSA and MSSA. Positive MRSA or MSSA clinical cultures and/or a MRSA- or MSSA-specific International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification (ICD-9/10 CM) diagnosis codes from adult inpatients (aged ≥18 years) were included as S. aureus hospitalizations. Septicemia was defined as a positive blood culture or a MRSA or MSSA septicemia code. Sensitivity and PPV for codes were calculated for hospitalizations where admission status was not listed as transfer; true infection was considered a positive clinical culture. Negative binominal regression models measured trends in rates of MRSA and MSSA per 1,000 hospital discharges. Results: We identified 168,634 MRSA and 148,776 MSSA hospitalizations in 256 hospitals; 17% of MRSA and 21% of MSSA were septicemia. Less than half of all S. aureus hospitalizations (49% MRSA, 46% MSSA) and S. aureus septicemia hospitalizations (37% MRSA, 38% MSSA) had both a positive culture and diagnosis code (Fig. 1). Sensitivity of MRSA codes in identifying positive cultures was 61% overall and 56% for septicemia, PPV was 62% overall and 53% for septicemia. MSSA codes had a sensitivity of 49% in identifying MSSA cultures and 52% for MSSA septicemia; PPV was 69% overall and 62% for septicemia. Despite low sensitivity, MRSA trends are similar for cultures and codes, and MSSA trends are divergent (Fig. 2). For hospitalizations with septicemia, mortality was highest among those with a blood culture only (31.3%) compared to hospitalizations with both a septicemia code and blood culture (16.6%), and septicemia code only (14.7%). Conclusions: ICD diagnosis code sensitivity and PPV for identifying infections were consistently poor in recent years. Less than half of hospitalizations have concordant microbiology laboratory results and diagnosis codes. Rates and trend estimates for MSSA differ by method of identification. Using diagnosis codes to identify S. aureus infections may not be appropriate for descriptive epidemiology or assessing trends due to significant misclassification.
Disclosures: Scott Fridkin reports that his spouse receives consulting fees from the vaccine industry.
Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) is a serious threat to patient safety due to limited treatment options and propensity to spread in healthcare settings. Using Emerging Infections Program (EIP) data, we describe changes in CRAB incidence and epidemiology. Methods: During January 2012 to December 2018, 9 sites (Colorado, Connecticut, Georgia, Maryland, Minnesota, New Mexico, New York, Oregon, and Tennessee) participated in active laboratory- and population-based surveillance. An incident case was defined as the first isolation of A. baumannii complex, in a 30-day period, resistant to ≥1 carbapenem (excluding ertapenem) from a normally sterile site or urine of a surveillance area resident. Cases were considered hospital-onset (HO) if the culture was collected >3 days after hospital admission; all others were community-onset (CO). Cases were classified as device-associated (DA) if the patient had 1 or more medical devices (ie, urinary catheter, central venous catheter (CVC), endotracheal/nasotracheal tube, tracheostomy, or another indwelling device) present in the 2 days prior to culture collection. Temporal trends were estimated using generalized linear models adjusted for age, race, sex, and EIP site. Results: Overall, 984 incident CRAB cases were identified, representing 849 patients. Among these patients, 291 (34%) were women, 510 (61%) were nonwhite, and the median age was 62 years (mean, 59; range, 0–102). Among the cases, 226 (23%) were HO; 758 (77%) were CO; and 793 (81%) were DA. Overall incidence rates in 2012 and 2018 were 1.58 (95% CI, 1.29–1.90) and 0.60 (95% CI, 0.40–0.67) per 100,000 population, respectively. There was a 15% annual decrease in incidence (adjusted rate ratio [aRR] 0.85; 95% CI: 0.82-0.88, P < .0001). Decreases were observed among sterile site (aRR 0.88; 95% CI, 0.84–0.93) and urine cases (aRR 0.83; 95% CI, 0.80–0.87). Annual decreases occurred for HO cases (aRR, 0.78; 95% CI, 0.73–0.85) and CO cases (aRR, 0.86; 95% CI, 0.83–0.9). The DA cases decreased 16% annually overall (aRR, 0.84; 95% CI, 0.81–0.88). Decreases among cases in patients with CVC (aRR, 0.85; 95% CI, 0.80–0.90) and urinary catheters (aRR, 0.84; 95% CI, 0.80–0.88) were smaller than what was seen in patients with other indwelling devices (aRR, 0.81; 95% CI, 0.77–0.86). Discussion: Overall, from 2012 to 2018, the incidence of CRAB decreased >60%. Decreases were observed in all case groups, regardless of source, infection onset location, or types of devices. Smaller annual decreases in rates of CO-CRAB than HO-CRAB suggest that there may be opportunities to accelerate prevention outside the hospital to further reduce the incidence of these difficult-to-treat infections.
Background: Microbiology data are utilized to quantify epidemiology and trends in pathogens, antimicrobial resistance, and bloodstream infections. Understanding variability and trends in rates of hospital-level blood culture utilization may be important for interpreting these findings. Methods: We used clinical microbiology results and discharge data to identify monthly blood culture rates from US hospitals participating in the Premier Healthcare Database during 2012–2017. We included all discharges from months where a hospital reported at least 1 blood culture with microbiology and antimicrobial susceptibility results. Blood cultures drawn on or before day 3 were defined as admission cultures (ACs); blood cultures collected after day 3 were defined as a postadmission cultures (PACs). The AC rate was defined as the proportion of all hospitalizations with an AC. The PAC rate was defined as the number of days with a PAC among all patient days. Generalized estimating equation regression models that accounted for hospital-level clustering with an exchangeable correlation matrix were used to measure associations of monthly rates with hospital bed size, teaching status, urban–rural designation, region, month, and year. The AC rates were modeled using logistic regression, and the PAC rates were modeled using a Poisson distribution. Results: We included 11.7 million hospitalizations from 259 hospitals, accounting for nearly 52 million patient days. The median annual hospital-level AC rate was 27.1%, with interhospital variation ranging from 21.1% (quartile 1) to 35.2% (quartile 3) (Fig. 1). Multivariable models revealed no significant trends over time (P = .74), but statistically significant associations between AC rates with month (P < .001) and region (P = .003), associations with teaching status (P = .063), and urban-rural designation (P = .083) approached statistical significance. There was no association with bed size (P = .38). The median annual hospital-level PAC rate was 11.1 per 1,000 patient days, and interhospital variability ranged from 7.6 (quartile 1) to 15.2 (quartile 3) (Fig. 2). Multivariable models of PAC rates showed no significant trends over time (P = .12). We found associations between PAC rates with month (P = .016), bed size (P = .030), and teaching status (P = .040). PAC rates were not associated with urban–rural designation (P = .52) or region (P = .29). Conclusions: Blood culture utilization rates in this large cohort of hospitals were unchanged between 2012 and 2017, though substantial interhospital variability was detected. Although both AC and PAC rates vary by time of year and potentially by teaching status, AC rates vary by geographic characteristics whereas PAC rates vary by bed size. These factors are important to consider when comparing rates of bloodstream infections by hospital.
Background: The Hospital-Acquired Condition Reduction Program (HACRP) is a pay-for-performance Medicare program that promotes reducing patient harm, particularly healthcare-associated infections (HAIs). We examined the association between infection-control–related activities and the number of penalties a hospital received between fiscal years 2015 and 2018. Methods: We used logistic regression with ordered categories to assess infection control resource use and the number of penalties, an ordered categorical dependent variable with 5 categories ranging from 0 to 4, as of 2018. Data sources included National Healthcare Safety Network, American Hospital Association Annual Survey, Medicare Impact and Cost Report files, and Data.Medicare.gov. We excluded hospitals lacking data to calculate any HACRP score or component score for HAI and hospitals missing observations for model variables (301 hospitals). We assessed the following model variables: teaching hospital status, infection preventionists (IP) per 1,000 beds, surveillance hours per week per bed, other infection control activities per week per bed, nurse-to-bed ratio, housekeeping expenditure per 10,000 beds, nursing position vacancies per bed, bed size, electronic health record (EHR) implementation, number of skilled nursing beds, rural or urban location, and Medicare patient case-mix (cmi_quartiles). Results: In our model, negative logit model point estimates indicated that increased values of the variable are associated with a lower odds of having a higher number of penalties. The final data set consisted of 3,004 US hospitals. Lower penalties were significantly associated with higher IP-to-bed ratio. Although the point estimates were <1, an association between lower penalties and higher nurse-to-bed ratios or electronic health records was not demonstrated (Table 1). Conclusions: Our results suggest that after controlling for selected hospital structural factors, incremental resources related to infection control have a protective association with HCARP penalties.
To compare risk of surgical site infection (SSI) following cesarean delivery between women covered by Medicaid and private health insurance.
Cesarean deliveries covered by Medicaid or private insurance and reported to the National Healthcare Safety Network (NHSN) and state inpatient discharge databases by hospitals in California (2011–2013).
Deliveries reported to NHSN and state inpatient discharge databases were linked to identify SSIs in the 30 days following cesarean delivery, primary payer, and patient and procedure characteristics. Additional hospital-level characteristics were obtained from public databases. Relative risk of SSI by primary payer primary payer was assessed using multivariable logistic regression adjusting for patient, procedure, and hospital characteristics, accounting for facility-level clustering.
Of 291,757 cesarean deliveries included, 48% were covered by Medicaid. SSIs were detected following 1,055 deliveries covered by Medicaid (0.75%) and 955 deliveries covered by private insurance (0.63%) (unadjusted odds ratio, 1.2; 95% confidence interval [CI], 1.1–1.3; P < .0001). The adjusted odds of SSI following cesarean deliveries covered by Medicaid was 1.4 (95% CI, 1.2–1.6; P < .0001) times the odds of those covered by private insurance.
In this, the largest and only multicenter study to investigate SSI risk following cesarean delivery by primary payer, Medicaid-insured women had a higher risk of infection than privately insured women. These findings suggest the need to evaluate and better characterize the quality of maternal healthcare for and needs of women covered by Medicaid to inform targeted infection prevention and policy.
To determine whether central line–associated bloodstream infections (CLABSIs) increase the likelihood of readmission.
Retrospective matched cohort study for the years 2008–2009.
Acute care hospitals.
Medicare recipients. CLABSI and readmission status were determined by linking National Healthcare Safety Network surveillance data to the Centers for Medicare and Medicaid Services’ Medical Provider and Analysis Review in 8 states. Frequency matching was used on International Classification of Diseases, Ninth Revision, Clinical Modification procedure code category and intensive care unit status.
We compared the rate of readmission among patients with and without CLABSI during an index hospitalization. Cox proportional hazard analysis was used to assess rate of readmission (the first hospitalization within 30 days after index discharge). Multivariate models included the following covariates: race, sex, length of index hospitalization stay, central line procedure code, Gagne comorbidity score, and individual chronic conditions.
Of the 8,097 patients, 2,260 were readmitted within 30 days (27.9%). The rate of first readmission was 7.1 events/person-year for CLABSI patients and 4.3 events/person-year for non-CLABSI patients (P<.001). The final model revealed a small but significant increase in the rate of 30-day readmissions for patients with a CLABSI compared with similar non-CLABSI patients. In the first readmission for CLABSI patients, we also observed an increase in diagnostic categories consistent with CLABSI, including septicemia and complications of a device.
Our analysis found a statistically significant association between CLABSI status and readmission, suggesting that CLABSI may have adverse health impact that extends beyond hospital discharge.